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Building an Intelligent Documentation Assistant with MongoDB-RAG

· 4 min read
Michael Lynn
Michael Lynn
Developer Advocate @ MongoDB

📖 TL;DR​

Ever wished your documentation could just answer questions directly instead of forcing users to sift through endless pages? That’s exactly what we built with the MongoDB-RAG Documentation Assistant. In this article, I’ll walk you through the why, what, and how of building a chatbot that retrieves precise, relevant information from MongoDB-RAG’s own documentation.

🤔 Why Build a Documentation Assistant?​

Traditional documentation search is useful, but it often leaves users with more questions than answers. Developers need to read through entire pages, infer context, and piece together solutions. Instead, we wanted something:

✅ Conversational – Answers questions in natural language
✅ Context-aware – Finds relevant documentation snippets instead of just keywords
✅ Fast & Accurate – Uses vector search to surface precise answers
✅ Transparent – Links to original sources so users can verify answers
✅ Scalable – Handles multiple LLM providers, including OpenAI and Ollama

Our solution? A chatbot powered by MongoDB-RAG, showcasing exactly what our tool was built for: retrieval-augmented generation (RAG) using MongoDB Atlas Vector Search.

🛠️ How We Built It​

We structured the assistant around four core components:

1️⃣ Document Ingestion​

To make documentation searchable, we need to process it into vector embeddings. We use semantic chunking to break long docs into meaningful pieces before ingestion.

const chunker = new Chunker({
  strategy: 'semantic',
  maxChunkSize: 500,
  overlap: 50
});

const documents = await loadMarkdownFiles('./docs');
const chunks = await Promise.all(
  documents.map(doc => chunker.chunkDocument(doc))
);

await rag.ingestBatch(chunks.flat());

📝 Why Semantic Chunking? Instead of blindly splitting text, we preserve contextual integrity by overlapping related sections.

2️⃣ Vector Search with MongoDB Atlas​

Once ingested, we use MongoDB Atlas Vector Search to find the most relevant documentation snippets based on a user’s query.

const searchResults = await rag.search(query, { 
  maxResults: 6,
  filter: { 'metadata.type': 'documentation' }
});

MongoDB’s $vectorSearch operator ensures we retrieve the closest matching content, ranked by relevance.

3️⃣ Streaming Responses for a Real Chat Experience​

To improve user experience, we stream responses incrementally as they’re generated.

router.post('/chat', async (req, res) => {
  const { query, history = [], stream = true } = req.body;
  
  const context = await ragService.search(query);
  
  if (stream) {
    res.writeHead(200, {
      'Content-Type': 'text/event-stream',
      'Cache-Control': 'no-cache',
      'Connection': 'keep-alive'
    });
    
    await llmService.generateResponse(query, context, history, res);
  } else {
    const answer = await llmService.generateResponse(query, context, history);
    res.json({ answer, sources: context });
  }
});

With this approach:

  • Responses appear in real-time instead of waiting for full generation 🚀
  • Developers can get partial answers quickly while longer responses load

4️⃣ Multi-Provider LLM Support​

The assistant supports multiple embedding providers, including OpenAI and self-hosted Ollama.

const config = {
  embedding: {
    provider: process.env.EMBEDDING_PROVIDER || 'openai',
    model: process.env.EMBEDDING_MODEL || 'text-embedding-3-small',
    baseUrl: process.env.OLLAMA_BASE_URL // For local deployment
  }
};

This allows users to switch providers easily, optimizing for performance, privacy, or cost.

💡 Key Features​

🔍 Real-time Context Retrieval​

Instead of guessing, the chatbot searches first and then generates answers.

🔗 Source Attribution​

Each response includes a link to the documentation, letting users verify answers.

⏳ Streaming Responses​

No waiting! Answers generate in real-time, improving responsiveness.

⚙️ Multi-Provider LLM Support​

Deploy with OpenAI for scale or Ollama for private, local hosting.

🤖 Fallback Handling​

If documentation doesn’t contain an answer, the chatbot transparently explains the limitation instead of fabricating responses.

🚀 Try It Yourself​

Want to build a MongoDB-RAG-powered assistant? Here’s how to get started:

1️⃣ Install MongoDB-RAG​

npm install mongodb-rag

2️⃣ Configure Your Environment​

MONGODB_URI=your_atlas_connection_string
EMBEDDING_PROVIDER=openai
EMBEDDING_API_KEY=your_api_key
EMBEDDING_MODEL=text-embedding-3-small

3️⃣ Initialize the Chatbot​

import { MongoRAG } from 'mongodb-rag';
import express from 'express';

const rag = new MongoRAG(config);
const app = express();

app.post('/api/chat', async (req, res) => {
  const { query } = req.body;
  const results = await rag.search(query);
  res.json({ answer: results });
});

🌩️ Production Considerations​

Where to Host?​

We deployed our assistant on Vercel for:

  • Serverless scalability
  • Fast global CDN
  • Easy Git-based deployments

Which LLM for Production?​

  • OpenAI – Best for reliability & speed
  • Ollama – Best for privacy-first self-hosted setups
EMBEDDING_PROVIDER=openai
EMBEDDING_MODEL=text-embedding-3-small

🔮 What’s Next?​

Future improvements include:

  • Better query reformulation to improve retrieval accuracy
  • User feedback integration to refine responses over time
  • Conversation memory for context-aware follow-ups

🎬 Conclusion​

By combining MongoDB Atlas Vector Search with modern LLMs, we built an assistant that transforms documentation into an interactive experience.

Try it out in our docs, and let us know what you think! 🚀

🔗 Resources​

📘 MongoDB-RAG Docs
🔗 GitHub Repository
📦 NPM Package